| Year |
Citation |
Score |
| 2020 |
Grinberg NF, Orhobor OI, King RD. An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat. Machine Learning. 109: 251-277. PMID 32174648 DOI: 10.1007/S10994-019-05848-5 |
0.384 |
|
| 2019 |
Sadawi N, Olier I, Vanschoren J, van Rijn JN, Besnard J, Bickerton R, Grosan C, Soldatova L, King RD. Multi-task learning with a natural metric for quantitative structure activity relationship learning Journal of Cheminformatics. 11. DOI: 10.1186/S13321-019-0392-1 |
0.336 |
|
| 2018 |
Olier I, Sadawi N, Bickerton GR, Vanschoren J, Grosan C, Soldatova L, King RD. Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Machine Learning. 107: 285-311. PMID 31997851 DOI: 10.1007/S10994-017-5685-X |
0.327 |
|
| 2017 |
Currin A, Korovin K, Ababi M, Roper K, Kell DB, Day PJ, King RD. Computing exponentially faster: implementing a non-deterministic universal Turing machine using DNA. Journal of the Royal Society, Interface. 14. PMID 28250099 DOI: 10.1098/Rsif.2016.0990 |
0.302 |
|
| 2015 |
Williams K, Bilsland E, Sparkes A, Aubrey W, Young M, Soldatova LN, De Grave K, Ramon J, de Clare M, Sirawaraporn W, Oliver SG, King RD. Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. Journal of the Royal Society, Interface / the Royal Society. 12: 20141289. PMID 25652463 DOI: 10.1098/Rsif.2014.1289 |
0.32 |
|
| 2014 |
Soldatova LN, Nadis D, King RD, Basu PS, Haddi E, Baumlé V, Saunders NJ, Marwan W, Rudkin BB. EXACT2: the semantics of biomedical protocols. Bmc Bioinformatics. 15: S5. PMID 25472549 DOI: 10.1186/1471-2105-15-S14-S5 |
0.313 |
|
| 2014 |
Zhou F, Toivonen H, King RD. The use of weighted graphs for large-scale genome analysis. Plos One. 9: e89618. PMID 24619061 DOI: 10.1371/Journal.Pone.0089618 |
0.3 |
|
| 2013 |
Scott IM, Lin W, Liakata M, Wood JE, Vermeer CP, Allaway D, Ward JL, Draper J, Beale MH, Corol DI, Baker JM, King RD. Merits of random forests emerge in evaluation of chemometric classifiers by external validation. Analytica Chimica Acta. 801: 22-33. PMID 24139571 DOI: 10.1016/J.Aca.2013.09.027 |
0.311 |
|
| 2013 |
Soldatova LN, Rzhetsky A, De Grave K, King RD. Representation of probabilistic scientific knowledge. Journal of Biomedical Semantics. 4: S7. PMID 23734675 DOI: 10.1186/2041-1480-4-S1-S7 |
0.369 |
|
| 2011 |
Whelan K, Ray O, King RD. Representation, simulation, and hypothesis generation in graph and logical models of biological networks. Methods in Molecular Biology (Clifton, N.J.). 759: 465-82. PMID 21863503 DOI: 10.1007/978-1-61779-173-4_26 |
0.333 |
|
| 2011 |
King RD. Rise of the robo scientists. Scientific American. 304: 72-7. PMID 21265330 DOI: 10.1038/Scientificamerican0111-72 |
0.304 |
|
| 2010 |
Scott IM, Vermeer CP, Liakata M, Corol DI, Ward JL, Lin W, Johnson HE, Whitehead L, Kular B, Baker JM, Walsh S, Dave A, Larson TR, Graham IA, Wang TL, ... King RD, et al. Enhancement of plant metabolite fingerprinting by machine learning. Plant Physiology. 153: 1506-20. PMID 20566707 DOI: 10.1104/Pp.109.150524 |
0.335 |
|
| 2010 |
Qi D, King RD, Hopkins AL, Bickerton GR, Soldatova LN. An ontology for description of drug discovery investigations. Journal of Integrative Bioinformatics. 7. PMID 20375446 DOI: 10.2390/Biecoll-Jib-2010-126 |
0.301 |
|
| 2010 |
Sparkes A, Aubrey W, Byrne E, Clare A, Khan MN, Liakata M, Markham M, Rowland J, Soldatova LN, Whelan KE, Young M, King RD. Towards Robot Scientists for autonomous scientific discovery. Automated Experimentation. 2: 1. PMID 20119518 DOI: 10.1186/1759-4499-2-1 |
0.322 |
|
| 2009 |
King RD, Rowland J, Oliver SG, Young M, Aubrey W, Byrne E, Liakata M, Markham M, Pir P, Soldatova LN, Sparkes A, Whelan KE, Clare A. The automation of science. Science (New York, N.Y.). 324: 85-9. PMID 19342587 DOI: 10.1126/Science.1165620 |
0.353 |
|
| 2008 |
Whelan KE, King RD. Using a logical model to predict the growth of yeast. Bmc Bioinformatics. 9: 97. PMID 18269749 DOI: 10.1186/1471-2105-9-97 |
0.344 |
|
| 2008 |
Coghill GM, Srinivasan A, King RD. Qualitative system identification from imperfect data Journal of Artificial Intelligence Research. 32: 825-877. DOI: 10.1613/Jair.2374 |
0.34 |
|
| 2008 |
Srinivasan A, King RD. Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming Journal of Machine Learning Research. 9: 1475-1533. DOI: 10.1145/1390681.1442781 |
0.336 |
|
| 2007 |
Buttingsrud B, King RD, Alsberg BK. An alignment-free methodology for modelling field-based 3D-structure activity relationships using inductive logic programming Journal of Chemometrics. 21: 509-519. DOI: 10.1002/Cem.1056 |
0.395 |
|
| 2006 |
Buttingsrud B, Ryeng E, King RD, Alsberg BK. Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships. Journal of Computer-Aided Molecular Design. 20: 361-73. PMID 17054018 DOI: 10.1007/S10822-006-9058-Y |
0.348 |
|
| 2006 |
Ferré S, King RD. Finding motifs in protein secondary structure for use in function prediction. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. 13: 719-31. PMID 16706721 DOI: 10.1089/Cmb.2006.13.719 |
0.376 |
|
| 2006 |
Clare A, Karwath A, Ougham H, King RD. Functional bioinformatics for Arabidopsis thaliana. Bioinformatics (Oxford, England). 22: 1130-6. PMID 16481336 DOI: 10.1093/Bioinformatics/Btl169 |
0.391 |
|
| 2005 |
King RD, Garrett SM, Coghill GM. On the use of qualitative reasoning to simulate and identify metabolic pathways. Bioinformatics (Oxford, England). 21: 2017-26. PMID 15647297 DOI: 10.1093/Bioinformatics/Bti255 |
0.351 |
|
| 2004 |
Whelan KE, King RD. Intelligent software for laboratory automation. Trends in Biotechnology. 22: 440-5. PMID 15331223 DOI: 10.1016/J.Tibtech.2004.07.010 |
0.314 |
|
| 2004 |
King RD, Wise PH, Clare A. Confirmation of data mining based predictions of protein function. Bioinformatics (Oxford, England). 20: 1110-8. PMID 14764546 DOI: 10.1093/Bioinformatics/Bth047 |
0.398 |
|
| 2004 |
King RD, Whelan KE, Jones FM, Reiser PG, Bryant CH, Muggleton SH, Kell DB, Oliver SG. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature. 427: 247-52. PMID 14724639 DOI: 10.1038/Nature02236 |
0.335 |
|
| 2004 |
King RD. Applying inductive logic programming to predicting gene function Ai Magazine. 25: 57-68. DOI: 10.1609/Aimag.V25I1.1747 |
0.411 |
|
| 2003 |
Toivonen H, Srinivasan A, King RD, Kramer S, Helma C. Statistical evaluation of the Predictive Toxicology Challenge 2000-2001. Bioinformatics (Oxford, England). 19: 1183-93. PMID 12835260 DOI: 10.1093/Bioinformatics/Btg130 |
0.373 |
|
| 2002 |
Karwath A, King RD. Homology induction: the use of machine learning to improve sequence similarity searches. Bmc Bioinformatics. 3: 11. PMID 11972320 DOI: 10.1186/1471-2105-3-11 |
0.372 |
|
| 2002 |
Clare A, King RD. Machine learning of functional class from phenotype data. Bioinformatics (Oxford, England). 18: 160-6. PMID 11836224 DOI: 10.1093/Bioinformatics/18.1.160 |
0.39 |
|
| 2002 |
Marchand-Geneste N, Watson KA, Alsberg BK, King RD. New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors. Journal of Medicinal Chemistry. 45: 399-409. PMID 11784144 DOI: 10.1021/Jm0155244 |
0.38 |
|
| 2001 |
King RD, Karwath A, Clare A, Dehaspe L. The utility of different representations of protein sequence for predicting functional class. Bioinformatics (Oxford, England). 17: 445-54. PMID 11331239 DOI: 10.1093/Bioinformatics/17.5.445 |
0.372 |
|
| 2001 |
King RD, Srinivasan A, Dehaspe L. Warmr: a data mining tool for chemical data. Journal of Computer-Aided Molecular Design. 15: 173-81. PMID 11272703 DOI: 10.1023/A:1008171016861 |
0.388 |
|
| 2001 |
Helma C, King RD, Kramer S, Srinivasan A. The predictive toxicology challenge 2000-2001 Bioinformatics. 17: 107-108. DOI: 10.1093/Bioinformatics/17.1.107 |
0.342 |
|
| 2000 |
King RD, Karwath A, Clare A, Dehaspe L. Accurate prediction of protein functional class from sequence in the Mycobacterium tuberculosis and Escherichia coli genomes using data mining. Yeast (Chichester, England). 17: 283-93. PMID 11119305 DOI: 10.1002/1097-0061(200012)17:4<283::Aid-Yea52>3.0.Co;2-F |
0.365 |
|
| 2000 |
Ouali M, King RD. Cascaded multiple classifiers for secondary structure prediction. Protein Science : a Publication of the Protein Society. 9: 1162-76. PMID 10892809 DOI: 10.1110/Ps.9.6.1162 |
0.374 |
|
| 2000 |
King RD, Ouali M, Strong AT, Aly A, Elmaghraby A, Kantardzic M, Page D. Is it better to combine predictions? Protein Engineering. 13: 15-9. PMID 10679525 DOI: 10.1093/Protein/13.1.15 |
0.363 |
|
| 2000 |
Kell DB, King RD. On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends in Biotechnology. 18: 93-8. PMID 10675895 DOI: 10.1016/S0167-7799(99)01407-9 |
0.347 |
|
| 2000 |
Alsberg BK, Marchand-Geneste N, King RD. A new 3D molecular structure representation using quantum topology with application to structure–property relationships Chemometrics and Intelligent Laboratory Systems. 54: 75-91. DOI: 10.1016/S0169-7439(00)00101-5 |
0.347 |
|
| 1999 |
Srinivasan A, King RD. Feature construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity Aided by Structural Attributes Data Mining and Knowledge Discovery. 3: 37-57. DOI: 10.1023/A:1009815821645 |
0.371 |
|
| 1998 |
King RD. Drug design, protein secondary structure prediction and functional genomics Acm Sigbio Newsletter. 18: 5-5. DOI: 10.1145/956034.956040 |
0.376 |
|
| 1997 |
King RD, Srinivasan A. The discovery of indicator variables for QSAR using inductive logic programming. Journal of Computer-Aided Molecular Design. 11: 571-80. PMID 9491349 DOI: 10.1023/A:1007967728701 |
0.362 |
|
| 1997 |
King RD, Saqi M, Sayle R, Sternberg MJ. DSC: public domain protein secondary structure predication. Computer Applications in the Biosciences : Cabios. 13: 473-4. PMID 9283763 DOI: 10.1093/Bioinformatics/13.4.473 |
0.359 |
|
| 1996 |
King RD, Srinivasan A. Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming. Environmental Health Perspectives. 104: 1031-40. PMID 8933051 DOI: 10.1289/Ehp.96104S51031 |
0.391 |
|
| 1996 |
King RD, Sternberg MJ. Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Science : a Publication of the Protein Society. 5: 2298-310. PMID 8931148 DOI: 10.1002/Pro.5560051116 |
0.38 |
|
| 1996 |
King RD, Muggleton SH, Srinivasan A, Sternberg MJ. Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences of the United States of America. 93: 438-42. PMID 8552655 DOI: 10.1073/Pnas.93.1.438 |
0.363 |
|
| 1996 |
Srinivasan A, Muggleton SH, Sternberg MJE, King RD. Theories for mutagenicity: a study in first-order and feature-based induction Artificial Intelligence. 85: 277-299. DOI: 10.1016/0004-3702(95)00122-0 |
0.364 |
|
| 1995 |
King RD, Feng C, Sutherland A. Statlog: Comparison Of Classification Algorithms On Large Real-World Problems Applied Artificial Intelligence. 9: 289-333. DOI: 10.1080/08839519508945477 |
0.305 |
|
| 1995 |
King RD, Hirst JD, Sternberg MJE. Comparison of artificial intelligence methods for modeling pharmaceutical QSARS Applied Artificial Intelligence. 9: 213-233. DOI: 10.1080/08839519508945474 |
0.381 |
|
| 1995 |
King RD, Sternberg MJE, Srinivasan A. Relating chemical activity to structure: An examination of ILP successes New Generation Computing. 13: 411-433. DOI: 10.1007/Bf03037220 |
0.37 |
|
| 1994 |
Hirst JD, King RD, Sternberg MJ. Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines. Journal of Computer-Aided Molecular Design. 8: 405-20. PMID 7815092 DOI: 10.1007/Bf00125375 |
0.352 |
|
| 1994 |
Sternberg MJ, King RD, Lewis RA, Muggleton S. Application of machine learning to structural molecular biology. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 344: 365-71. PMID 7800706 DOI: 10.1098/Rstb.1994.0075 |
0.415 |
|
| 1994 |
King RD, Clark DA, Shirazi J, Sternberg MJ. On the use of machine learning to identify topological rules in the packing of beta-strands. Protein Engineering. 7: 1295-303. PMID 7700861 DOI: 10.1093/Protein/7.11.1295 |
0.32 |
|
| 1994 |
Bratko I, King R. Applications of inductive logic programming Intelligence\/Sigart Bulletin. 5: 43-49. DOI: 10.1145/181668.181678 |
0.334 |
|
| 1993 |
King RD, Hirst JD, Sternberg MJE. New approaches to QSAR: Neural networks and machine learning Perspectives in Drug Discovery and Design. 1: 279-290. DOI: 10.1007/Bf02174529 |
0.33 |
|
| 1992 |
Schulze-Kremer S, King RD. IPSA-Inductive Protein Structure Analysis. Protein Engineering. 5: 377-90. PMID 1518785 DOI: 10.1093/Protein/5.5.377 |
0.333 |
|
| 1992 |
Muggleton S, King RD, Sternberg MJ. Protein secondary structure prediction using logic-based machine learning. Protein Engineering. 5: 647-57. PMID 1480619 DOI: 10.1093/Protein/5.7.647 |
0.398 |
|
| 1992 |
King RD, Muggleton S, Lewis RA, Sternberg MJ. Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences of the United States of America. 89: 11322-6. PMID 1454814 DOI: 10.1073/Pnas.89.23.11322 |
0.381 |
|
| 1992 |
Sternberg MJ, Lewis RA, King RD, Muggleton S. Modelling the structure and function of enzymes by machine learning. Faraday Discussions. 269-80. PMID 1290938 DOI: 10.1039/Fd9929300269 |
0.407 |
|
| 1990 |
King RD, Sternberg MJ. Machine learning approach for the prediction of protein secondary structure. Journal of Molecular Biology. 216: 441-57. PMID 2254939 DOI: 10.1016/S0022-2836(05)80333-X |
0.364 |
|
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